Methods and devices for grading a tumor

ABSTRACT

Method and system for grading a tumor. For example, a system for grading a tumor comprising: an image obtaining module configured to obtain a pathological image of a tissue to be examined; a snippet obtaining module configured to obtain one or more snippets having one or more sizes from the pathological image; an analyzing module configured to obtain one or more classification features based on at least analyzing the one or more snippets using one or more selected trained detection models of the analyzing module, wherein each selected trained detection model is configured to identify one or more classification features; and an outputting module configured to determine a tumor identification result based on at least the one or more classification features and output the tumor identification result.

1. CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.201910146428.X, filed Feb. 27, 2019, incorporated by reference hereinfor all purposes.

2. BACKGROUND OF THE INVENTION

Certain embodiments of the present invention are directed to imageprocessing. More particularly, some embodiments of the invention providesystems and methods for grading a tumor. Merely by way of example, someembodiments of the invention have been applied to diagnosing a medicalimage. But it would be recognized that the invention has a much broaderrange of applicability.

Gliomas, also known as Glioblastomas, are the most common primarymalignant brain tumors produced by brain and spinal glial cancer,accounting for approximately 46% of intracranial tumors. In 1998, theWorld Health Organization announced the ranking of mortality accordingto the severity of mortality, within which, malignant Glioma is listedas the second leading cause of death in patients under 34-year-old andis the third leading cause of death in cancer patients aged 35-54 yearsold. Patients with benign Glioblastoma are known to see slow growth witha long course of disease, with the average time from symptom onset tovisitation being about two years. In contrast, malignant Gliomas growfast and have short course of disease, with most having under 3 monthsfrom the onset of symptoms to the visitation, leading to the health of70%-80% of endangered patients within half a year (e.g., from symptomonset).

The clinical symptoms of brain glioma can be divided into twocategories, one includes symptoms of increased intracranial pressure,such as headache, vomiting, vision loss, diplopia, mental symptoms,etc., the second includes tumor compression, infiltration, focal lesionsymptoms corresponding to destruction of brain tissue, earlymanifestations of irritating symptoms such as localized epilepsy, andlater manifestations of neurological deficit symptoms such as sputum.When the above symptoms occur, a patient is to attend a hospitalpromptly.

A traditional method for diagnosing a Glioblastoma type includes: (1)biopsy, (2) sectioning, (3) staining, (4) recognizing by an experienceddoctor, and (5) diagnosing a result. Biopsy, also known as surgicalpathology, refers to the technique of taking out diseased tissue fromthe patient by cutting, clamping or puncture, and performingpathological examination the taken out diseased tissue. Commonly, thetissue taken out by biopsy is cut into very thin slices, such as afterformaldehyde fixation, dehydration, paraffin embedding, etc., and thencan be made into a slice for examination under a microscope, such asusing steps of patching, baking, dewaxing, hydration, dyeing, etc. Insome use cases, the purpose of staining is to render differentstructures within the cell tissue to appear in different colors for easeof viewing. For example, after staining, different organelles, theirinclusions, and the different types of cell tissues can be displayeddistinctly. Once prepared, a pathologist typically first observes thespecimen with the naked eye, then observes the specimen under amicroscope, and then provide a diagnostic result according to thecomprehensive analysis of the taken pathological tissue. Finally, thepathologist gives a pathology report based on the observations and theanalysis.

The described traditional method for diagnosing a Glioblastoma type in apathological image (or section thereof) has at least the followingdrawbacks: (1) some tumor features are not easy to identify, such as incases with nuclear divisions, which may be missed by inexperienceddoctors while still requiring senior doctors excessive time to identify(e.g., by thoroughly analyzing each image one by one); and (2) for eachslide showing a slice of the tumor, a doctor is typically trained togradually narrow down the search target until a feature is located. Asduring this process, different doctors have different ways andpreferences of viewing (e.g., selection and/or displaying of viewingareas), inconsistent diagnosis between doctors and/or biopsy samples arecommon.

There is therefore a need for improved methods and systems for grading atumor, such as a tumor type, with improved accuracy.

3. BRIEF SUMMARY OF THE INVENTION

Certain embodiments of the present invention are directed to imageprocessing. More particularly, some embodiments of the invention providesystems and methods for grading a tumor. Merely by way of example, someembodiments of the invention have been applied to diagnosing a medicalimage. But it would be recognized that the invention has a much broaderrange of applicability.

In various embodiments, a system for grading a tumor includes an imageobtaining module configured to obtain a pathological image of a tissueto be examined; a snippet obtaining module configured to obtain one ormore snippets having one or more sizes from the pathological image; ananalyzing module configured to obtain one or more classificationfeatures based on at least analyzing the one or more snippets using oneor more selected trained detection models of the analyzing module,wherein each selected trained detection model is configured to identifyone or more classification features; and an outputting module configuredto determine a tumor identification result based on at least the one ormore classification features and output the tumor identification result.

In some embodiments, the snippet obtaining module is configured toobtain one or more snippets having one or more sizes from thepathological image based on at least one or more input or specifiedsizes.

In some embodiments, the system further includes a model selectingmodule configured to provide one or more detection model sets eachincluding one or more trained detection models. In some examples, theanalyzing module is configured to use a selected detection model setselected from the one or more detection model sets for obtaining the oneor more classification features, the selected detection model setincluding the one or more selected trained detection models, each of theone or more classification features corresponding to one of the one ormore selected trained detection models.

In some embodiments, the model selecting module is further configured toselect the selected detection model set from the one or more detectionmodel sets based on at least an input or specified body part.

In some embodiments, the snippet obtaining module is further configuredto: determine the one or more sizes of the one or more snippets based onat least one of the one or more selected trained detection models; andobtain the one or more snippets having the determined one or more sizesfrom the pathological image.

In some embodiments, the snippet obtaining module is further configuredto receive size information associated with the one or more selectedtrained detection models from the analyzing module; determine the one ormore sizes of the one or more snippets based on at least the one or moreselected trained detection models; obtain the one or more snippetshaving the determined one or more sizes from the pathological image; andoutput the obtained one or more snippets to the analyzing module.

In some embodiments, the snippet obtaining module includes a microscopicdevice.

In some embodiments, the analyzing module is configured to obtain afirst classification feature (e.g., egg-shaped cell or nuclear division)associated with a first snippet of a first size using a first traineddetection model; and to obtain a second classification feature (e.g.,cell necrosis or vascular endothelial cell proliferation) associatedwith a second snippet of a second size using a second trained detectionmodel, the second size being larger than the first size.

In some embodiments, the tumor identification result includes at leastone selected from a group consisting of a tumor type and a tumor class.

In some examples, the system further includes a model training moduleconfigured to: receive a training image having at least a first trainingsnippet of a first size and a second training snippet of a second size;receive one or more classification features associated with the trainingimage, the one or more classification features includes a firstclassification feature associated with the first training snippet of thefirst size and a second classification feature associated with thesecond training snippet of the second size; and train one or moredetection models based at least in part on the one or moreclassification features to generate the one or more trained detectionmodels; wherein a first trained detection model of the one or moretrained detection models corresponds to the first classification featureand the first size; and wherein a second trained detection model of theone or more trained detection models corresponds to the secondclassification feature and the second size.

In various embodiments, a computer-implemented method for grading atumor includes: obtaining a pathological image of a tissue to beexamined using an image obtaining module; obtaining one or more snippetshaving one or more sizes from the pathological image using a snippetobtaining module; obtaining one or more classification features based onat least analyzing the one or more snippets using one or more selectedtrained detection models of an analyzing module, wherein each selectedtrained detection model is configured to identify one or moreclassification features; and determining a tumor identification resultbased on at least the identified one or more classification features andoutputting the tumor identification result using an outputting module.

In some embodiments, the method further includes obtaining one or moresnippets having one or more sizes from the pathological image using thesnippet obtaining module based on at least one or more input orspecified sizes.

In some embodiments, obtaining one or more classification features basedon at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module includes:selecting a selected detection model set from one or more detectionmodel sets each including one or more trained detection models using amodel selecting module, the selected detection model set including theone or more selected trained detection models; and obtaining the one ormore classification features each corresponding to one of the one ormore selected trained detection models using the analyzing module.

In some embodiments, the method further includes selecting the detectionmodel set based on at least an input or specified body part using themodel selecting module.

In some embodiments, the method further includes determining the one ormore sizes of the one or more snippets based on at least one of the oneor more selected trained detection models; and obtaining the one or moresnippets having the determined one or more sizes from the pathologicalimage.

In some embodiments, the method further includes receiving sizeinformation associated with the one or more selected trained detectionmodels from the analyzing module; determining the one or more sizes ofthe one or more snippets based on at least the one or more selectedtrained detection models; obtaining the one or more snippets having thedetermined one or more sizes from the pathological image; and outputtingthe obtained one or more snippets to the analyzing module.

In some embodiments, determining a tumor identification result based onat least the one or more classification features and outputting thetumor identification result using an outputting module includes:determining the tumor identification result including at least oneselected from a group consisting of a tumor type and a tumor class basedon at least the one or more classification features and outputting thetumor identification result using the outputting module.

In various embodiments, a non-transitory computer-readable medium withinstructions stored thereon, that when executed by a processor, performthe processes including: obtaining a pathological image of a tissue tobe examined using an image obtaining module; obtaining one or moresnippets having one or more sizes from the pathological image using asnippet obtaining module; obtaining one or more classification featuresbased on at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module, wherein eachselected trained detection model is configured to identify one or moreclassification features; and determining a tumor identification resultbased on at least the identified one or more classification features andoutputting the tumor identification result using an outputting module.

In some embodiments, the non-transitory computer-readable medium, whenexecuted, further perform the process of obtaining one or more snippetshaving one or more sizes from the pathological image using the snippetobtaining module based on at least one or more input or specified sizes.

In some embodiments, obtaining one or more classification features basedon at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module includes:selecting a selected detection model set from one or more detectionmodel sets each including one or more trained detection models using amodel selecting module, the selected detection model set including theone or more selected trained detection models; and obtaining the one ormore classification features each corresponding to one of the one ormore selected trained detection models using the analyzing module.

In some embodiments, the non-transitory computer-readable medium, whenexecuted, further perform the process of selecting the selecteddetection model set based on at least an input or specified body partusing the model selecting module.

In some embodiments, the non-transitory computer-readable medium, whenexecuted, further perform the processes including: determining the oneor more sizes of the one or more snippets based on at least one of theone or more selected trained detection models; and obtaining the one ormore snippets having the determined one or more sizes from thepathological image.

Depending upon embodiment, one or more benefits may be achieved. Thesebenefits and various additional objects, features and advantages of thepresent invention can be fully appreciated with reference to thedetailed description and accompanying drawings that follow.

4. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified diagram showing a system for grading a tumor,according to some embodiments of the present invention.

FIG. 1B is a simplified diagram showing another system for grading atumor, according to some embodiments of the present invention.

FIG. 2A is a simplified diagram showing an interface, according to someembodiments of the present invention.

FIG. 2B is a simplified diagram showing another interface, according tosome embodiments of the present invention.

FIG. 3 is a representative view of two pathological images of differentsizes, according to some embodiments of the present invention.

FIG. 4A is a representative view of an “egg-shaped” cell, according tosome embodiments of the present invention.

FIG. 4B is a representative view of nuclear division, according to someembodiments of the present invention.

FIG. 4C is a representative view of cell necrosis, according to someembodiments of the present invention.

FIG. 4D is a representative view of proliferation of vascularendothelial cells, according to some embodiments of the presentinvention.

FIG. 4E is a representative view of an Oligodendroglioma, according tosome embodiments of the present invention.

FIG. 4F is a representative view of Astrocytoma, according to someembodiments of the present invention.

FIG. 5 is a simplified diagram showing a method for grading a tumor,according to some embodiments of the present invention.

FIG. 6 is a simplified diagram showing a method for training ananalyzing module, according to some embodiments of the presentinvention.

5. DETAILED DESCRIPTION OF THE INVENTION

Certain embodiments of the present invention are directed to imageprocessing. More particularly, some embodiments of the invention providesystems and methods for grading a tumor. Merely by way of example, someembodiments of the invention have been applied to diagnosing a medicalimage. But it would be recognized that the invention has a much broaderrange of applicability.

Embodiments of the present invention disclose a tumor determinationsystem, method, and storage medium. In some examples, the technicalsolutions described in the embodiments of the invention can help reduceproblems pertaining determining tumor types relying on the experience ofa doctor, which conventionally results to low accuracy with room forimprovement. In certain examples, the determining of the tumor type isperformed automatically. In various examples, the system is configuredto determine (e.g., automatically) a tumor (e.g., a tumor type and/orclass) according to a pathologist's diagnosis procedures.

Pathological sections are considered as the gold standard for doctors todiagnose a condition pertaining an inclusion of a tumor. For example, apathologist typically gives a diagnosis by examining the patient'sbiological tissue sample, such as after performing detailed analysis onthe tissue sample to obtain a tumor type or class. Taking brain gliomaas an example, after obtaining a pathological image of the tumor, apathologist can observe the quantity of “egg-shaped” cell units, nucleardivisions, cell necrosis and vascular endothelial cell proliferations inthe pathological image, and further determine a tumor type and tumorclass. In various embodiments, the described methods and systems arebased on at least a pathologist's diagnosis procedure to achieveautomatic determining of tumor type and class. Tumor class may bereferred to as tumor level or tumor stage.

In certain embodiments, a system for grading a tumor includes an imageobtaining module configured to obtain a pathological image of a tissueto be examined; a snippet obtaining module configured to obtain one ormore snippets having one or more sizes from the pathological image; ananalyzing module configured to obtain one or more classificationfeatures based on at least analyzing the one or more snippets using oneor more selected trained detection models of the analyzing module,wherein each selected trained detection model is configured to identifyone or more classification features; and an outputting module configuredto determine a tumor identification result based on at least the one ormore classification features and output the tumor identification result.

In certain examples, the snippet obtaining module is configured toobtain one or more snippets having one or more sizes from thepathological image based on at least one or more input or specifiedsizes.

In certain examples, the system further includes a model selectingmodule configured to provide one or more detection model sets eachincluding one or more trained detection models; wherein the analyzingmodule is configured to use a selected detection model set selected fromthe one or more detection model sets for obtaining the one or moreclassification features, the selected detection model set including theone or more selected trained detection models, each of the one or moreclassification features corresponding to one of the one or more selectedtrained detection models.

In certain examples, the model selecting module is further configured toselect the selected detection model set from the one or more detectionmodel sets based on at least an input or specified body part.

In certain examples, the snippet obtaining module is further configuredto: determine the one or more sizes of the one or more snippets based onat least one of the one or more selected trained detection models; andobtain the one or more snippets having the determined one or more sizesfrom the pathological image.

In certain examples, the snippet obtaining module includes a microscopicdevice.

In certain examples, the tumor identification result includes at leastone selected from a group consisting of a tumor type and a tumor class.

In certain embodiments, a computer-implemented method for grading atumor includes: obtaining a pathological image of a tissue to beexamined using an image obtaining module; obtaining one or more snippetshaving one or more sizes from the pathological image using a snippetobtaining module; obtaining one or more classification features based onat least analyzing the one or more snippets using one or more selectedtrained detection models of an analyzing module, wherein each selectedtrained detection model is configured to identify one or moreclassification features; and determining a tumor identification resultbased on at least the identified one or more classification features andoutputting the tumor identification result using an outputting module.

In certain examples, obtaining one or more classification features basedon at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module includesselecting a selected detection model set from one or more detectionmodel sets each including one or more trained detection models using amodel selecting module, the selected detection model set including theone or more selected trained detection models; and obtaining the one ormore classification features each corresponding to one of the one ormore selected trained detection models using the analyzing module.

In certain embodiments, a non-transitory computer-readable medium withinstructions stored thereon, that when executed by a processor, performthe processes including: obtaining a pathological image of a tissue tobe examined using an image obtaining module; obtaining one or moresnippets having one or more sizes from the pathological image using asnippet obtaining module; obtaining one or more classification featuresbased on at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module, wherein eachselected trained detection model is configured to identify one or moreclassification features; and determining a tumor identification resultbased on at least the identified one or more classification features andoutputting the tumor identification result using an outputting module.

FIG. 1A is a simplified diagram showing a system 10 for grading a tumor,according to some embodiments of the present invention. This diagram ismerely an example, which should not unduly limit the scope of theclaims. One of ordinary skill in the art would recognize manyvariations, alternatives, and modifications. In some examples, thesystem 10 includes an image obtaining module 11, a snippet obtainingmodule 12, an analyzing module 13, and an outputting module 14. Althoughthe above has been shown using a selected group of components for thesystem, there can be many alternatives, modifications, and variations.For example, some of the components may be expanded and/or combined.Other components may be inserted to those noted above. Depending uponthe embodiment, the arrangement of components may be interchanged withothers replaced.

In various embodiments, the image obtaining module 11 is configured toobtain or acquire an image (e.g., a pathological image), such as animage of a tissue to be inspected or examined. In various embodiments,the snippet obtaining module 12 (which may be referred to as a partialimage obtaining module) is configured to obtain, acquire, or extract oneor more snippets from the image of the tissue to be inspected. Invarious examples, each snippet is smaller or equal to the image in sizeor volume. In some examples, the one or more snippets have one or moresizes. A snippet may also be referred to as a partial image, asub-image, a subimage, a local image, or an analysis image. In variousembodiments, the analyzing module 13 is configured to analyze the one ormore snippets based on at least one or more selected trained detectionmodels to obtain one or more (e.g., a plurality of) classificationfeatures. In some embodiments, each of the one or more selected traineddetection model is configured to determine one or more classificationfeatures. A detection model may also be referred to as an analyzingmodel. In various embodiments, the outputting module 14 is configured todetermine and output a tumor identification result based on at least theone or more classification features.

In some embodiments, the image (e.g., the pathological image) is a CTimage or a MR image of the tissue to be examined, or a microscopic imageof a slice of the tissue to be examined (e.g., as shown in FIGS. 3 and4A-4F).

FIG. 1B is a simplified diagram showing another system 10′ for grading atumor, according to some embodiments of the present invention. Thisdiagram is merely an example, which should not unduly limit the scope ofthe claims. One of ordinary skill in the art would recognize manyvariations, alternatives, and modifications. In some examples, thesystem 10′ includes the image obtaining module 11, the snippet obtainingmodule 12 (may also be referred to as an analysis-image obtainingmodule), the analyzing module 13, the outputting module 14, and notablyadditional to that of system 10, a storage module 15 and a modelselecting module 16. Although the above has been shown using a selectedgroup of components for the system, there can be many alternatives,modifications, and variations. For example, some of the components maybe expanded and/or combined. Other components may be inserted to thosenoted above. Depending upon the embodiment, the arrangement ofcomponents may be interchanged with others replaced.

In various embodiments, the storage module 15 is configured to store oneor more images (e.g., pathological images) or one or more snippets(e.g., analysis images).

In various embodiments, the image obtaining module 11 includes a readingmodule configured to receive and/or read the pathological image of thetissue to be examined. In some embodiments, the image obtaining module11 is configured to send the pathological image to the storage module 15and/or store the pathological image in the storage module 15. In certainembodiments, the image obtaining module 11 includes a microscopic device(e.g., a microscope) configured to acquire (e.g., directly) thepathological image of the tissue to be examined. In some examples, whenthe image obtaining module 11 includes a microscope device, the systemfor grading a tumor includes an image reading module for receiving thepathological image of the tissue to be examined from the microscope, andfurther including a storage module for storing the pathological image.

FIG. 2A is a simplified diagram showing an interface 17, according tosome embodiments of the present invention. This diagram is merely anexample, which should not unduly limit the scope of the claims. One ofordinary skill in the art would recognize many variations, alternatives,and modifications. In some examples, the interface 17 includes a sizeselection menu, a model selection menu, and a result displaying region.Although the above has been shown using a selected group of componentsfor the system, there can be many alternatives, modifications, andvariations. For example, some of the components may be expanded and/orcombined. Other components may be inserted to those noted above.Depending upon the embodiment, the arrangement of components may beinterchanged with others replaced.

In certain embodiments, a system for grading a tumor (e.g., system 10)is configured to receive one or more sizes inputted or entered by auser, such as via a size selection menu or box in an interface (e.g., ahuman-computer interaction) 17. In some examples, the one or more sizesis inputted before an image obtaining module 12 extracts one or moreanalysis images from a pathological image. In certain embodiments, theimage obtaining module 12 is configured to obtain the one or moreanalysis images based on at least the one or more sizes inputted by theuser. In various examples, one or more default sizes are set (e.g.,predetermined), which when set, the snippet obtaining module 12 isconfigured to obtain the one or more snippets from the pathologicalimage based on at least the one or more default sizes. In certainexamples, the snippet obtaining module 12 is further configured to storethe obtained one or more snippets in the storage module 15, such as in adesignated storage space. In various embodiments, the use of one or moredefault sizes simplifies and accelerates the process of obtainingsnippet(s).

In some embodiments, the snippet obtaining module 12 is configured toextract the one or more analysis images from the pathological imagethrough a moving or sliding window. As an example, to obtain twoanalysis images of two different sizes, a user can use a pre-configuredsliding window (e.g., with multiple corresponding snippet sizes) toobtain a first snippet (e.g., analysis image), such as with a size of512×512, and a second snippet with a size of 1024×1024, such as with adegree of overlap set at 10% for one or both of the analysis images.Accordingly, FIG. 3 is a representative view of two analysis images ofdifferent sizes, taken from the same pathological image, according tosome embodiments of the present invention. In some examples, thepathological image and/or the snippet is two-dimensional orthree-dimensional.

Returning to FIG. 1B, in various embodiments, the model selecting module16 is configured to provide a user with a plurality of trained detectionmodels (e.g., selectable trained detection models). In various examples,the outputting module 14 is configured to determine the tumoridentification result based on at least the one or more classificationfeatures (which may also be referred to as characteristic features). Insome examples, the model selecting module 16 is configured to select atrained detection model from plurality of (multiple) trained detectionmodels to obtain the one or more classification features. For example,each selected trained detection model is configured to determine one ormore classification features. In certain examples, the analyzing module13 is configured to control or use the one or more selected traineddetection models to obtain the one or more classification features, suchas by analyzing the obtained one or more snippets and/or thepathological image directly. In certain embodiments, when a user selectsa detection model set (may also be referred to as a group of detectionmodels), such as via the model selecting module 16, the user selects oneor more selected trained detection models included in the selecteddetection model set.

FIG. 2B is a simplified diagram showing another interface 17′, accordingto some embodiments of the present invention. This diagram is merely anexample, which should not unduly limit the scope of the claims. One ofordinary skill in the art would recognize many variations, alternatives,and modifications. In some examples, the interface 17′ includes a sizeselection menu, a model selection menu, a result displaying region, anda body selection menu. Although the above has been shown using aselected group of components for the system, there can be manyalternatives, modifications, and variations. For example, some of thecomponents may be expanded and/or combined. Other components may beinserted to those noted above. Depending upon the embodiment, thearrangement of components may be interchanged with others replaced. Invarious embodiments, the body selection menu is configured to enable auser to input a selected body part. In some embodiments, each detectionmodel set corresponds to a different body part, wherein a user canselect a detection model set by selecting a body part (e.g., a head, achest, an abdomen, a pelvic cavity), such as using the model selectingmodule 16 and/or the body selection menu in the interface 17′.

In certain embodiments, a user can select different detection model setsfor each human body parts, which can use different algorithms indetermining a tumor in an image. In certain examples, a user can selecta body part via the interface 17′, such as via the body part selectionmenu, and select a detection model set via the model selecting module 16(e.g., via the model selection menu on an interface), such as based onthe selected body part. In some examples, each trained detection modelis constructed based on a deep learning model, such as a convolutionalneural network model, such as YOLO, Fast R-CNN, UNet, VNet, or FCN.

In some examples, each trained detection model corresponds to one ormore sizes, such as one or more snippet sizes. In certain examples, thesnippet obtaining module is configured to receive the selected detectionmodel set selected by the user in the model selecting module 16,determine the specified one or more sizes of the analysis images, andextract the one or more analysis images of the corresponding one or moresizes from the pathological image of the tissue to be examined accordingto the sizes specified.

In various examples, after a user determines the detection model set,one or more trained detection model is determined, and the snippetobtaining module 12 is configured to extract the one or more analysisimages of the corresponding one or more sizes according to the userrequirement, wherein the analyzing module 13 is configured to controlthe one or more selected trained detection models to analyze the one ormore analysis images to obtain the one or more classification features,wherein each trained detection model is configured to determine one ormore classification features. For example, each trained detection modelis configured to determine one classification feature.

In various embodiments, brain glioma includes one or more cellclassification features such as egg-shaped cell units, nuclear division,cell necrosis, and proliferation of vascular endothelial cells. FIG. 4Ais a representative view of an egg-shaped cell, according to someembodiments of the present invention. FIG. 4B is a representative viewof nuclear division (e.g., mitotic or non-mitotic), according to someembodiments of the present invention. FIG. 4C is a representative viewof cell necrosis, according to some embodiments of the presentinvention. FIG. 4D is a representative view of proliferation of vascularendothelial cells, according to some embodiments of the presentinvention.

In some examples, when a detection model set includes a traineddetection model and a user selects two analysis images of two sizes(e.g., analysis sizes) for analysis, the analyzing module is configuredto input the two analysis images into the trained detection model,wherein the trained detection model is configured to receive and analyzethe analysis images and output an identification result, such as basedon at least one or more classification features identified in theanalysis images. In certain examples, the one or more classificationfeatures includes egg-shaped cell units (e.g., at least a certainquantity), nuclear division, cell necrosis, and/or vascular endothelialcell proliferation. In various examples, a selected trained detectionmodel is configured to analyze a small-sized snippet to determine thepresence of egg-shaped cells (e.g., in respect to a certain quantity)and nuclear division, whereas the same selected trained detection modelor another selected trained detection model is configured to analyze alarge-sized snippet to determine the presence of cell necrosis and/orvascular endothelial cell proliferation.

In various embodiments, a selected trained detection model set includesa first selected trained detection model, a second selected traineddetection model, a third selected trained detection mode, and a fourthselected trained detection model. In certain examples, the selectedtrained detection model set is configured to analyze two analysis imagesof two sizes, wherein the two analysis images are, in some examples,extracted from the same pathological image. In some examples, the firstselected trained detection model is configured to analyze a firstanalysis image having a small-size to determine whether the firstanalysis image includes one or more egg-shaped cell units. In someexamples, the second selected trained detection model is configured toanalyze the first analysis image having the small-size to determinewhether the second analysis image includes mitotic nuclear divisionevents. In some examples, the third selected trained detection model isconfigured to analyze the first analysis image having the small-size todetermine whether the second analysis image includes non-mitotic nucleardivision events. In some examples, the fourth selected trained detectionmodel is configured to analyze a second analysis image having thelarge-size to determine the presence of cell necrosis and/or vascularendothelial cell proliferation. In various examples, each selectedtrained detection model, or the selected trained detection modelscollectively, is configured to output an identification resultcorresponding to one or more classification features.

In various embodiments, an identification result includes a tumoridentification result, which in some examples, includes a tumor type(which may be referred to as tumor category) and/or a tumor class (whichmay be referred to as tumor stage, or tumor grade, a tumor level). Insome examples, the identification result and/or the tumor identificationresult is determined based on one or more features detected. As anexample, in a use case for determining brain glioma, the tumoridentification result includes a tumor type selected from a groupconsisting of Oligodendroglioma (e.g., as shown in FIG. 4E) andAstrocytoma (e.g., as shown in FIG. 4F). Further using the use case fordetermining brain glioma as an example, the tumor class is selected froma group consisting of Oligodendroglioma (e.g., WHO Class II, for moreinformation, please refer to the World Health Organization centralnervous system tumor classification method) and anaplasticOligodendroglioma, Astrocytoma (e.g., WHO Class II), Glioblastoma (e.g.,WHO Class IV), and anaplastic Astrocytoma (e.g., WHO Class III).

Further using the use case for determining brain glioma as an example,the outputting module 14 is configured to receive the tumoridentification result based on the one or more classificationcharacteristics determined by the analyzing module 13. In certainexamples, when a certain quantity (e.g., greater than a certainthreshold quantity) of egg-shaped cell units (as a classificationfeature) are identified, the outputting module 14 is configured todetermine the tumor identification result to include a tumor type beingOligodendroglioma. In some examples, when the quantity of egg-shapedcell units (as a classification feature) identified is low (e.g., belowa certain threshold quantity), the outputting module 14 is configured todetermine the tumor identification result to include a tumor type beingAstrocytoma. In various examples, when a certain quantity (e.g., beyonda certain threshold quantity) of egg-shaped cell units (as aclassification feature) are identified, and that no phenomenon of cellnecrosis and vascular endothelial cell proliferation are recognized, theoutputting module 14 is configured to determine the tumor identificationresult to be Oligodendroglioma. In various examples, when a certainquantity (e.g., beyond a certain threshold quantity) of egg-shaped cellunits (as a classification feature) are identified, and that phenomenonof cell necrosis and/or vascular endothelial cell proliferation isrecognized, the outputting module 14 is configured to determine thetumor identification result to be anaplastic (or variable)Oligodendroglioma.

In some embodiments, when the quantity of egg-shaped cell unitsrecognized is below a threshold value, no nuclear division event isrecognized, and that no phenomenon of cell necrosis or vascularendothelial cell proliferation is recognized, the outputting module 14is configured to determine a tumor identification result to beAstrocytoma (WHO Class II). In some embodiments, when the quantity ofegg-shaped cell units recognized is below a threshold value, no nucleardivision event is recognized, and that phenomenon of cell necrosisand/or vascular endothelial cell proliferation is recognized, theoutputting module 14 is configured to determine a tumor identificationresult to be Glioblastoma. In some embodiments, when the quantity ofegg-shaped cell units recognized is below a threshold value, one or morenuclear division events is recognized, and that phenomenon of cellnecrosis and/or vascular endothelial cell proliferation is recognized,the outputting module 14 is configured to determine a tumoridentification result to be Glioblastoma (WHO Class IV). In someembodiments, when the quantity of egg-shaped cell units recognized isbelow a threshold value, one or more nuclear division events isrecognized, and that phenomenon of cell necrosis and vascularendothelial cell proliferation are not recognized, the outputting module14 is configured to determine a tumor identification result to beanaplastic Astrocytoma (WHO Class III).

In some embodiments, the outputting module 14 is configured to outputthe tumor recognition or identification result, which in variousexamples, includes a tumor type and/or a tumor class. In certainexamples, the tumor identification result is displayed in a resultdisplay region of an interface (e.g., interface 17 or 17′), which canhelp a doctor to understand the identification result quickly andclearly.

In certain embodiments, the technical solutions provided by the systemfor grading a tumor help reduce issues associated with determining atumor type relying on the experience of a doctor, such as low accuracy.In contrast, in various embodiments, the proposed system is configuredto automatically classify or determine the type and/or class of a tumorin a pathological image.

FIG. 5 is a simplified diagram showing a method S100 for determining(e.g., automatically) a tumor, according to some embodiments of thepresent invention. This diagram is merely an example, which should notunduly limit the scope of the claims. One of ordinary skill in the artwould recognize many variations, alternatives, and modifications. Insome examples, the method S100 includes a process S110 of obtaining apathological image of a tissue to be examined using the image obtainingmodule, a process S120 of obtaining one or more snippets having one ormore sizes from the pathological image using the snippet obtainingmodule, a process S130 of obtaining one or more classification featuresbased on at least analyzing the one or more snippets using one or moreselected trained detection models of an selected analyzing module,wherein each selected trained detection model is configured to identifyone or more classification features, and a process S140 of determining atumor identification result based on at least the one or moreclassification features and outputting the tumor identification result(e.g., including a tumor type and/or a tumor class) using an outputtingmodule. Although the above has been shown using a selected group ofprocesses for the method, there can be many alternatives, modifications,and variations. For example, some of the processes may be expandedand/or combined. Other processes may be inserted to those noted above.Depending upon the embodiment, the sequence of processes may beinterchanged with others replaced. In some embodiments, the method S100is implemented by a system (e.g., system 10) for grading a tumor an/orimplemented by appropriate software and/or hardware.

In various embodiments, the process S110 of obtaining a pathologicalimage of a tissue to be examined using the image obtaining module (e.g.,image obtaining module 11) includes storing the pathological image intoa storage module (e.g., storage module 15) of the system for grading atumor. In some examples, the process S110 of obtaining a pathologicalimage of a tissue to be examined using the image obtaining module 11includes obtaining the pathological image of the tissue to be examinedusing a microscopic device of the image obtaining module 11. In certainexamples, the process S110 includes staining or dying a biopsy slice andplacing the stained slice into the microscopic device (e.g., onto amicro-motion observation platform) for examination using the microscopicdevice (e.g., one having a photographing function) to obtain thepathological image.

In certain embodiments, the process S110 includes controlling themicroscopic device to photograph tumor slices based on at leasttraversal movement to obtain a pathological image. In certain examples,different pathological images may be obtained have overlapping regions(e.g., of a human body). In certain examples, the system for grading atumor includes an image reading module and/or an image splicing modulefor reading or receiving a pathological image of the tissue to beexamined from the image obtaining module 11, and then storing thepathological image into the storage module 15. In some examples, theimage splicing module is configured to use a preset image splicingalgorithm based on feature extraction to splice the full large-scalepathological image include multiple snippets, which can be smaller thanthe image.

In various embodiments, the process S120 includes using the snippetobtaining module 12 to obtain the one or more snippets having the one ormore sizes by moving (e.g., sliding) an acquisition window on thepathological image. In certain examples, moving the acquisition windowincludes moving multiple sliding windows of multiple sizes, such assequentially. In some examples, obtaining snippets having differentsizes help improve tumor recognition efficiency and/or accuracy. Forexample, analyzing a snippet having a large-size enables analyzing ofgreater quantity of classification features, which in some examples, canincrease accuracy but may reduce efficiency; whereas, analyzing asnippet having a small-size enables analyzing of lesser quantity ofclassification features, which in some examples, can decrease accuracybut may increase efficiency.

As an example, in a use case of analyzing two analysis images of twosizes, a small-size with an image block obtained with an overlap of 10%and a size of 512×512 from a full pathological image (e.g., using apreset sliding window) and a large-size with an image block obtainedwith an overlap of 10% and a size of 1024×1024 from the fullpathological image, as shown in FIG. 3. In some examples, the degree ofoverlap and image block size can be changed.

In various embodiments, the process S130 includes extracting the one ormore snippets (analysis images) having corresponding one or more sizes(e.g., using the snippet obtaining module 12) according to a user input.In certain embodiments, the analyzing module 13 is configured to use oneor more selected trained detection models (e.g., selected by the user)to analyze the one or more snippets to obtain one or more classificationfeatures. In some examples, each selected trained detection model isconfigured to determine one or more classification features. Forexample, each selected trained detection model is configured todetermine one classification feature.

In various embodiments, the process S140 includes receiving or obtainingthe one or more classification features and generating a tumoridentification result based on at least the one or more classificationfeatures. In certain examples, the tumor identification result includesa tumor type and/or a tumor class.

In some examples, the process S140 includes displaying or outputting(e.g., using the outputting module 14) the tumor identification resultin an interface (e.g., interface 17 or interface 17′), such as in theresult displaying region. In certain examples, the process S140 furtherincludes outputting and/or displaying the one or more classificationfeatures, such as alongside the tumor identification result, which mayhelp a doctor to clearly and/or efficiently see the relationship betweenthe tumor identification result and the one or more classificationcharacteristics to provide explanatory and persuasive analysis logicbeneficial to the doctor.

FIG. 6 is a simplified diagram showing a method S200 for training ananalyzing module, according to some embodiments of the presentinvention. This diagram is merely an example, which should not undulylimit the scope of the claims. One of ordinary skill in the art wouldrecognize many variations, alternatives, and modifications. In someexamples, the method S200 includes a process S210 of establishing ananalyzing module and a process S220 of training the establishedanalyzing module. Although the above has been shown using a selectedgroup of processes for the method, there can be many alternatives,modifications, and variations. For example, some of the processes may beexpanded and/or combined. Other processes may be inserted to those notedabove. Depending upon the embodiment, the sequence of processes may beinterchanged with others replaced.

In various embodiments, a detection model, such as a trained detectionmodel, such as a selected trained detection model is constructed basedon deep learning algorithms, such as YOLO, Fast R-CNN, UNet, VNet, andFCN algorithms of convolutional neural networks. In some embodiments, adetection model set, such as a trained detection model set, such as aselected trained detection model set includes multiple detection modelsconstructed based on a YOLO algorithm. In certain embodiments, adetection model is configured to first perform feature extraction on aninput analysis image using a feature extraction network (e.g., neuralnetwork), obtaining a feature map with a fixed size (e.g., m×m),dividing the analysis image into m×m grid cells (which may also bereferred to as grid units), and if a central coordinate of a target inthe ground truth with the correct marking falls in one of the gridcells, the grid cell is used to predict or analyze the target.

In some examples, each grid unit has a fixed number of borders, forexample, 3 borders in a YOLO v3-based model, where only the largestborder standard in the gold standard (IOU) is used to predict thetarget. In various examples, the gold standard is interpreted asfollows: in supervised learning, data is marked appearing in the form of(x, t), where x is the input data and t is the marking. Gold standard iswhen a correct marking is assigned to t, and not a gold standard when awrong marking is assigned to t. In certain examples, coordinates of aborder can be predicted by the following formula:b _(x)=σ(t _(x))+c _(x)b _(y)=σ(t _(y))+c _(y)b _(ω) =p _(ω) e ^(t) ^(ω)b _(h) =p _(h) e ^(t) ^(h)where t_(x), t_(y), t_(w), t_(h) represent the predicted outputs of adetection model; c_(x) and c_(y) represents he coordinates of the gridunits, for example, c_(x) is zero and c_(y) is one when representing agrid unit in row zero and column one; p_(w), p_(h) represent the sizesof predicted front borders; and b_(x), b_(y), b_(w), b_(h) represent thecenter coordinates of the predicted borders and the sizes of thepredicted borders.

In various embodiments, before training a detection model, acorresponding image size and a corresponding classification feature aredetermined. For example, each detection model corresponds to oneclassification feature and two image sizes. In some embodiments, such asfor determining brain glioma, five trained detection models areestablished (e.g., in process S210) to identify their correspondingclassification features.

In some examples, the process S220 includes receiving diagnostic inputsfrom multiple pathologists (e.g., experienced pathologists), which insome examples, includes one or more manually marked classificationfeatures for each diagnosed pathological image. In some examples, thediagnostic inputs are performed on N₁ sheets of small-size analysisimages with a size of 512×512 containing one or more classificationfeatures and/or N₂ sheets of large-size analysis images with a size of1024×1024 containing one or more classification features, such asobtained from a pathological image through one or more sliding windowsin an interface. In certain examples, the process S220 includesinputting the small-size analysis images and/or the large-size analysisimages into corresponding one or more trained detection models toidentify one or more classification features. In various examples, theone or more trained detection model corresponds to one or moreclassification features, such that a system for grading a tumor isconfigured to select and use the one or more trained detection models todetermine or identify one or more classification features, which, incertain examples, can then be used to determine a tumor identificationresult.

In various embodiments, a non-transitory computer-readable medium withinstructions stored thereon, that when executed by a processor, performthe processes including: obtaining a pathological image of a tissue tobe examined using an image obtaining module; obtaining one or moresnippets having one or more sizes from the pathological image using asnippet obtaining module; obtaining one or more classification featuresbased on at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module, wherein eachselected trained detection model is configured to identify one or moreclassification features; and determining a tumor identification resultbased on at least the identified one or more classification features andoutputting the tumor identification result using an outputting module.

In various embodiments, a system for grading a tumor includes an imageobtaining module configured to obtain a pathological image of a tissueto be examined; a snippet obtaining module configured to obtain one ormore snippets having one or more sizes from the pathological image; ananalyzing module configured to obtain one or more classificationfeatures based on at least analyzing the one or more snippets using oneor more selected trained detection models of the analyzing module,wherein each selected trained detection model is configured to identifyone or more classification features; and an outputting module configuredto determine a tumor identification result based on at least the one ormore classification features and output the tumor identification result.In some examples, the system is implemented according to at least thesystem 10 of FIG. 1A and/or the system 10′ of FIG. 1B. In certainexamples, the system is configured to perform at least the method S100of FIG. 5 and/or the method S200 of FIG. 6.

In some embodiments, the snippet obtaining module is configured toobtain one or more snippets having one or more sizes from thepathological image based on at least one or more input or specifiedsizes.

In some embodiments, the system further includes a model selectingmodule configured to provide one or more detection model sets eachincluding one or more trained detection models. In some examples, theanalyzing module is configured to use a selected detection model setselected from the one or more detection model sets for obtaining the oneor more classification features, the selected detection model setincluding the one or more selected trained detection models, each of theone or more classification features corresponding to one of the one ormore selected trained detection models.

In some embodiments, the model selecting module is further configured toselect the selected detection model set from the one or more detectionmodel sets based on at least an input or specified body part.

In some embodiments, the snippet obtaining module is further configuredto: determine the one or more sizes of the one or more snippets based onat least one of the one or more selected trained detection models; andobtain the one or more snippets having the determined one or more sizesfrom the pathological image.

In some embodiments, the snippet obtaining module is further configuredto receive size information associated with the one or more selectedtrained detection models from the analyzing module; determine the one ormore sizes of the one or more snippets based on at least the one or moreselected trained detection models; obtain the one or more snippetshaving the determined one or more sizes from the pathological image; andoutput the obtained one or more snippets to the analyzing module.

In some embodiments, the snippet obtaining module includes a microscopicdevice.

In some embodiments, the analyzing module is configured to obtain afirst classification feature (e.g., egg-shaped cell or nuclear division)associated with a first snippet of a first size using a first traineddetection model; and to obtain a second classification feature (e.g.,cell necrosis or vascular endothelial cell proliferation) associatedwith a second snippet of a second size using a second trained detectionmodel, the second size being larger than the first size.

In some embodiments, the tumor identification result includes at leastone selected from a group consisting of a tumor type and a tumor class.

In some examples, the system further includes a model training moduleconfigured to: receive a training image having at least a first trainingsnippet of a first size and a second training snippet of a second size;receive one or more classification features associated with the trainingimage, the one or more classification features includes a firstclassification feature associated with the first training snippet of thefirst size and a second classification feature associated with thesecond training snippet of the second size; and train one or moredetection models based at least in part on the one or moreclassification features to generate the one or more trained detectionmodels; wherein a first trained detection model of the one or moretrained detection models corresponds to the first classification featureand the first size; and wherein a second trained detection model of theone or more trained detection models corresponds to the secondclassification feature and the second size.

In various embodiments, a computer-implemented method for grading atumor includes: obtaining a pathological image of a tissue to beexamined using an image obtaining module; obtaining one or more snippetshaving one or more sizes from the pathological image using a snippetobtaining module; obtaining one or more classification features based onat least analyzing the one or more snippets using one or more selectedtrained detection models of an analyzing module, wherein each selectedtrained detection model is configured to identify one or moreclassification features; and determining a tumor identification resultbased on at least the identified one or more classification features andoutputting the tumor identification result using an outputting module.In some examples, the method is implemented according to at the methodS100 of FIG. 5. In certain examples, the method is implemented by atleast the system 10 of FIG. 1A and/or the system 10′ of FIG. 1B.

In some embodiments, the method further includes obtaining one or moresnippets having one or more sizes from the pathological image using thesnippet obtaining module based on at least one or more input orspecified sizes.

In some embodiments, obtaining one or more classification features basedon at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module includes:selecting a selected detection model set from one or more detectionmodel sets each including one or more trained detection models using amodel selecting module, the selected detection model set including theone or more selected trained detection models; and obtaining the one ormore classification features each corresponding to one of the one ormore selected trained detection models using the analyzing module.

In some embodiments, the method further includes selecting the detectionmodel set based on at least an input or specified body part using themodel selecting module.

In some embodiments, the method further includes determining the one ormore sizes of the one or more snippets based on at least one of the oneor more selected trained detection models; and obtaining the one or moresnippets having the determined one or more sizes from the pathologicalimage.

In some embodiments, the method further includes receiving sizeinformation associated with the one or more selected trained detectionmodels from the analyzing module; determining the one or more sizes ofthe one or more snippets based on at least the one or more selectedtrained detection models; obtaining the one or more snippets having thedetermined one or more sizes from the pathological image; and outputtingthe obtained one or more snippets to the analyzing module.

In some embodiments, determining a tumor identification result based onat least the one or more classification features and outputting thetumor identification result using an outputting module includes:determining the tumor identification result including at least oneselected from a group consisting of a tumor type and a tumor class basedon at least the one or more classification features and outputting thetumor identification result using the outputting module.

In various embodiments, a non-transitory computer-readable medium withinstructions stored thereon, that when executed by a processor, performthe processes including: obtaining a pathological image of a tissue tobe examined using an image obtaining module; obtaining one or moresnippets having one or more sizes from the pathological image using asnippet obtaining module; obtaining one or more classification featuresbased on at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module, wherein eachselected trained detection model is configured to identify one or moreclassification features; and determining a tumor identification resultbased on at least the identified one or more classification features andoutputting the tumor identification result using an outputting module.In some examples, the non-transitory computer-readable medium withinstructions stored thereon is implemented according to the method S100of FIG. 5 and/or the method S200 of FIG. 6. In certain examples, thenon-transitory computer-readable medium with instructions stored thereonis implemented by a computer (e.g., a terminal).

In some embodiments, the non-transitory computer-readable medium, whenexecuted, further perform the process of obtaining one or more snippetshaving one or more sizes from the pathological image using the snippetobtaining module based on at least one or more input or specified sizes.

In some embodiments, obtaining one or more classification features basedon at least analyzing the one or more snippets using one or moreselected trained detection models of an analyzing module includes:selecting a selected detection model set from one or more detectionmodel sets each including one or more trained detection models using amodel selecting module, the selected detection model set including theone or more selected trained detection models; and obtaining the one ormore classification features each corresponding to one of the one ormore selected trained detection models using the analyzing module.

In some embodiments, the non-transitory computer-readable medium, whenexecuted, further perform the process of selecting the selecteddetection model set based on at least an input or specified body partusing the model selecting module.

In some embodiments, the non-transitory computer-readable medium, whenexecuted, further perform the processes including: determining the oneor more sizes of the one or more snippets based on at least one of theone or more selected trained detection models; and obtaining the one ormore snippets having the determined one or more sizes from thepathological image.

For example, some or all components of various embodiments of thepresent invention each are, individually and/or in combination with atleast another component, implemented using one or more softwarecomponents, one or more hardware components, and/or one or morecombinations of software and hardware components. In another example,some or all components of various embodiments of the present inventioneach are, individually and/or in combination with at least anothercomponent, implemented in one or more circuits, such as one or moreanalog circuits and/or one or more digital circuits. In yet anotherexample, while the embodiments described above refer to particularfeatures, the scope of the present invention also includes embodimentshaving different combinations of features and embodiments that do notinclude all of the described features. In yet another example, variousembodiments and/or examples of the present invention can be combined.

Additionally, the methods and systems described herein may beimplemented on many different types of processing devices by programcode comprising program instructions that are executable by the deviceprocessing subsystem. The software program instructions may includesource code, object code, machine code, or any other stored data that isoperable to cause a processing system to perform the methods andoperations described herein. Other implementations may also be used,however, such as firmware or even appropriately designed hardwareconfigured to perform the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, datainput, data output, intermediate data results, final data results, etc.)may be stored and implemented in one or more different types ofcomputer-implemented data stores, such as different types of storagedevices and programming constructs (e.g., RAM, ROM, EEPROM, Flashmemory, flat files, databases, programming data structures, programmingvariables, IF-THEN (or similar type) statement constructs, applicationprogramming interface, etc.). It is noted that data structures describeformats for use in organizing and storing data in databases, programs,memory, or other computer-readable media for use by a computer program.

The systems and methods may be provided on many different types ofcomputer-readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, DVD, etc.)that contain instructions (e.g., software) for use in execution by aprocessor to perform the methods' operations and implement the systemsdescribed herein. The computer components, software modules, functions,data stores and data structures described herein may be connecteddirectly or indirectly to each other in order to allow the flow of dataneeded for their operations. It is also noted that a module or processorincludes a unit of code that performs a software operation and can beimplemented for example as a subroutine unit of code, or as a softwarefunction unit of code, or as an object (as in an object-orientedparadigm), or as an applet, or in a computer script language, or asanother type of computer code. The software components and/orfunctionality may be located on a single computer or distributed acrossmultiple computers depending upon the situation at hand.

The computing system can include client devices and servers. A clientdevice and server are generally remote from each other and typicallyinteract through a communication network. The relationship of clientdevice and server arises by virtue of computer programs running on therespective computers and having a client device-server relationship toeach other.

This specification contains many specifics for particular embodiments.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations, one or more features from a combination can in some casesbe removed from the combination, and a combination may, for example, bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Although specific embodiments of the present invention have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the invention is not to belimited by the specific illustrated embodiments.

What is claimed is:
 1. A system for grading a tumor, the systemcomprising: an image obtaining module configured to obtain apathological image of a tissue to be examined; a snippet obtainingmodule configured to obtain one or more snippets having one or moresizes from the pathological image; an analyzing module configured toobtain one or more classification features based on at least analyzingthe one or more snippets using one or more selected trained detectionmodels of the analyzing module, wherein each selected trained detectionmodel is configured to identify one or more classification features; anoutputting module configured to determine a tumor identification resultbased on at least the one or more classification features and output thetumor identification result; a model selecting module configured toprovide one or more detection model sets each including one or moretrained detection models; and wherein the analyzing module is configuredto use a selected detection model set selected from the one or moredetection model sets for obtaining the one or more classificationfeatures, the selected detection model set including the one or moreselected trained detection models, each of the one or moreclassification features corresponding to one of the one or more selectedtrained detection models.
 2. The system of claim 1, wherein the snippetobtaining module is configured to obtain one or more snippets having oneor more sizes from the pathological image based on at least one or moreinput or specified sizes.
 3. The system of claim 1, wherein the modelselecting module is further configured to select the selected detectionmodel set from the one or more detection model sets based on at least aninput or specified body part.
 4. The system of claim 3, wherein thesnippet obtaining module is further configured to: determine the one ormore sizes of the one or more snippets based on at least one of the oneor more selected trained detection models; and obtain the one or moresnippets having the determined one or more sizes from the pathologicalimage.
 5. The system of claim 3, wherein the snippet obtaining module isfurther configured to: receive size information associated with the oneor more selected trained detection models from the analyzing module;determine the one or more sizes of the one or more snippets based on atleast the one or more selected trained detection models; obtain the oneor more snippets having the determined one or more sizes from thepathological image; and output the obtained one or more snippets to theanalyzing module.
 6. The system of claim 1, wherein the snippetobtaining module includes a microscopic device.
 7. The system of claim1, wherein the analyzing module is configured to: obtain a firstclassification feature associated with a first snippet of a first sizeusing a first trained detection model, the first classification featureis egg-shaped cell or nuclear division; and obtain a secondclassification feature associated with a second snippet of a second sizeusing a second trained detection model, the second size being largerthan the first size, the second classification feature is cell necrosisor vascular endothelial cell proliferation.
 8. The system of claim 1,wherein the tumor identification result includes at least one selectedfrom a group consisting of a tumor type and a tumor class.
 9. The systemof claim 1, further includes a model training module configured to:receive a training image having at least a first training snippet of afirst size and a second training snippet of a second size; receive oneor more classification features associated with the training image, theone or more classification features includes a first classificationfeature associated with the first training snippet of the first size anda second classification feature associated with the second trainingsnippet of the second size; and train one or more detection models basedat least in part on the one or more classification features to generatethe one or more trained detection models; wherein a first traineddetection model of the one or more trained detection models correspondsto the first classification feature and the first size; and wherein asecond trained detection model of the one or more trained detectionmodels corresponds to the second classification feature and the secondsize.
 10. A computer-implemented method for grading a tumor, the methodcomprising: obtaining a pathological image of a tissue to be examinedusing an image obtaining module; obtaining one or more snippets havingone or more sizes from the pathological image using a snippet obtainingmodule; obtaining one or more classification features based on at leastanalyzing the one or more snippets using one or more selected traineddetection models of an analyzing module, wherein each selected traineddetection model is configured to identify one or more classificationfeatures; and determining a tumor identification result based on atleast the identified one or more classification features and outputtingthe tumor identification result using an outputting module; wherein theobtaining one or more classification features based on at leastanalyzing the one or more snippets using one or more selected traineddetection models of an analyzing module includes: selecting a selecteddetection model set from one or more detection model sets each includingone or more trained detection models using a model selecting module, theselected detection model set including the one or more selected traineddetection models; and obtaining the one or more classification featureseach corresponding to one of the one or more selected trained detectionmodels using the analyzing module.
 11. The method of claim 10, furtherincluding: obtaining one or more snippets having one or more sizes fromthe pathological image using the snippet obtaining module based on atleast one or more input or specified sizes.
 12. The method of claim 10,further including: selecting the detection model set based on at leastan input or specified body part using the model selecting module. 13.The method of claim 12, further including: determining the one or moresizes of the one or more snippets based on at least one of the one ormore selected trained detection models; and obtaining the one or moresnippets having the determined one or more sizes from the pathologicalimage.
 14. The method of claim 12, further including: receiving sizeinformation associated with the one or more selected trained detectionmodels from the analyzing module; determining the one or more sizes ofthe one or more snippets based on at least the one or more selectedtrained detection models; obtaining the one or more snippets having thedetermined one or more sizes from the pathological image; and outputtingthe obtained one or more snippets to the analyzing module.
 15. Themethod of claim 10, wherein the determining a tumor identificationresult based on at least the one or more classification features andoutputting the tumor identification result using an outputting moduleincludes: determining the tumor identification result including at leastone selected from a group consisting of a tumor type and a tumor classbased on at least the one or more classification features and outputtingthe tumor identification result using the outputting module.
 16. Anon-transitory computer-readable medium with instructions storedthereon, that when executed by a processor, perform the processesincluding: obtaining a pathological image of a tissue to be examinedusing an image obtaining module; obtaining one or more snippets havingone or more sizes from the pathological image using a snippet obtainingmodule; obtaining one or more classification features based on at leastanalyzing the one or more snippets using one or more selected traineddetection models of an analyzing module, wherein each selected traineddetection model is configured to identify one or more classificationfeatures; and determining a tumor identification result based on atleast the identified one or more classification features and outputtingthe tumor identification result using an outputting module; wherein theobtaining one or more classification features based on at leastanalyzing the one or more snippets using one or more selected traineddetection models of an analyzing module includes: selecting a selecteddetection model set from one or more detection model sets each includingone or more trained detection models using a model selecting module, theselected detection model set including the one or more selected traineddetection models; and obtaining the one or more classification featureseach corresponding to one of the one or more selected trained detectionmodels using the analyzing module.
 17. The non-transitorycomputer-readable medium of claim 16, when executed, further perform theprocess of: obtaining one or more snippets having one or more sizes fromthe pathological image using the snippet obtaining module based on atleast one or more input or specified sizes.